Name
Abstract | ||
|---|---|---|
Fine-grained object recognition aims at recognizing objects belonging to the same basic-level class such as dog, bird or fish, which is a challenging problem in computer vision. In this paper, we consider the problem of recognizing handbags that belong to a specific brand. In order to identify the subtle differences among handbags, we propose to enhance the handbag local structure pattern by using the Hölder exponent, and extract the feature from the enhanced handbag image to complement the feature extracted directly from the original handbag image. We term such two types of features as the complementary and original features. These features will then be fused by using Multiple Kernel Learning (MKL) for branded handbag recognition. We conduct the experiments on a newly built branded handbag dataset, the results of which demonstrate the effectiveness of the proposed complementary feature in recognizing the handbags. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7026191 | ICIP |
Keywords | Field | DocType |
handbag local structure pattern enhancement,fine-grained object recognition,branded handbag recognition,complementary feature extraction,branded handbag dataset,learning (artificial intelligence),handbag,hölder exponent,mkl,multiple kernel learning,enhanced handbag image,feature extraction,object recognition,computer vision,hölder exponent,image enhancement | Computer vision,Holder exponent,Pattern recognition,Computer science,Multiple kernel learning,Local structure,Feature extraction,Artificial intelligence,Cognitive neuroscience of visual object recognition | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Wang | 1 | 134 | 11.13 |
| Sheng Li | 2 | 54 | 13.29 |
| Alex C. Kot | 3 | 1096 | 92.07 |